Over the past decade, the amount of biomedical data available has grown at unprecedented
rates. Increased automation technology and larger data volumes have encouraged the use …

Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, several deep learning …

Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …

Modern therapeutic development often involves several stages that are interconnected, and
multiple iterations are usually required to bring a new drug to the market. Computational …

Molecular docking simulation is a very popular and well-established computational
approach and has been extensively used to understand molecular interactions between a …

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in
protein–ligand interaction modeling, hit identification and optimization, in which accurate …

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of
the most important targets for drug discovery. Current computational strategies mainly focus …

Prediction of protein–ligand binding affinities is a central issue in structure-based computer-
aided drug design. In recent years, much effort has been devoted to the prediction of the …

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …